39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
import random
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def append_to_next_population(survivor_method):
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return lambda ga: ga.population.append_children(survivor_method(ga))
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class Survivor_Selection:
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"""Survivor selection determines which individuals should be brought to the next generation"""
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def __append_to_next_population(survivor_method):
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return append_to_next_population(survivor_method)
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@append_to_next_population
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def fill_in_best(ga):
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"""Fills in the next population with the best chromosomes from the last population"""
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needed_amount = len(ga.population) - ga.population.total_children
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return ga.population[:needed_amount]
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@append_to_next_population
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def fill_in_random(ga):
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"""Fills in the next population with random chromosomes from the last population"""
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needed_amount = len(ga.population) - ga.population.total_children
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return [random.choice(ga.population) for n in range(needed_amount)]
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@append_to_next_population
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def fill_in_parents_then_random(ga):
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"""Fills in the next population with all parents followed by random chromosomes from the last population"""
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needed_amount = len(ga.population) - ga.population.total_children
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parent_amount = min(ga.population.total_parents, needed_amount)
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random_amount = needed_amount - parent_amount
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return ga.population.get_mating_pool()[:parent_amount] + [random.choice(ga.population) for n in range(random_amount)]
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